[Doc] Prepare v0.5.3 release (#351)

Author: cenyk1230

README.md

@@ -21,20 +21,22 @@ We summarize the contributions of CogDL as follows:
 
 ## ❗ News
 
+- The new **v0.5.3 release** supports mixed-precision training by setting \textit{fp16=True} and provides a basic [example](https://github.com/THUDM/cogdl/blob/master/examples/jittor/gcn.py) written by [Jittor](https://github.com/Jittor/jittor). It also updates the tutorial in the document, fixes downloading links of some datasets, and fixes potential bugs of operators. 
+
 - The new **v0.5.2 release** adds a GNN example for ogbn-products and updates geom datasets. It also fixes some potential bugs including setting devices, using cpu for inference, etc.
 
 - The new **v0.5.1 release** adds fast operators including SpMM (cpu version) and scatter_max (cuda version). It also adds lots of datasets for node classification which can be found in [this link](./cogdl/datasets/rd2cd_data.py). 🎉
 
 - The new **v0.5.0 release** designs and implements a unified training loop for GNN. It introduces `DataWrapper` to help prepare the training/validation/test data and `ModelWrapper` to define the training/validation/test steps. 🎉
 
-- The new **v0.4.1 release** adds the implementation of Deep GNNs and the recommendation task. It also supports new pipelines for generating embeddings and recommendation. Welcome to join our tutorial on KDD 2021 at 10:30 am - 12:00 am, Aug. 14th (Singapore Time). More details can be found in https://kdd2021graph.github.io/. 🎉
-
 <details>
 <summary>
 News History
 </summary>
 <br/>
 
+- The new **v0.4.1 release** adds the implementation of Deep GNNs and the recommendation task. It also supports new pipelines for generating embeddings and recommendation. Welcome to join our tutorial on KDD 2021 at 10:30 am - 12:00 am, Aug. 14th (Singapore Time). More details can be found in https://kdd2021graph.github.io/. 🎉
+
 - The new **v0.4.0 release** refactors the data storage (from `Data` to `Graph`) and provides more fast operators to speed up GNN training. It also includes many self-supervised learning methods on graphs. BTW, we are glad to announce that we will give a tutorial on KDD 2021 in August. Please see [this link](https://kdd2021graph.github.io/) for more details. 🎉
 
 - CogDL supports GNN models with Mixture of Experts (MoE). You can install [FastMoE](https://github.com/laekov/fastmoe) and try **[MoE GCN](./cogdl/models/nn/moe_gcn.py)** in CogDL now!

README_CN.md

@@ -21,20 +21,22 @@ CogDL的特性包括：
 
 ## ❗ 最新
 
+- 最新的 **v0.5.3 release** 支持混合精度(fp16)训练，提供了[Jittor](https://github.com/Jittor/jittor)的初步支持（见[example](https://github.com/THUDM/cogdl/blob/master/examples/jittor/gcn.py)）。这个版本更新了文档中的使用教程，修复了一部分数据集的下载链接，修复了某些算子在不同环境下可能的问题。
+
 - 最新的 **v0.5.2 release** 给ogbn-products数据集添加了GNN样例，更新了geom数据集。这个版本同时修复了一些潜在的问题，包括设置不同device，使用cpu进行预测等。
 
 - 最新的 **v0.5.1 release** 添加了一些高效的算子，包括cpu版本的SpMM和cuda版本的scatter_max。这个版本同时增加了很多用于节点分类的[数据集](./cogdl/datasets/rd2cd_data.py)。 🎉
 
 - 最新的 **v0.5.0 release** 为图神经网络的训练设计了一套统一的流程. 这个版本去除了原先的`Task`类，引入了`DataWrapper`来准备training/validation/test过程中所需的数据，引入了`ModelWrapper`来定义模型training/validation/test的步骤. 🎉
 
-- 最新的 **v0.4.1 release** 增加了深层GNN的实现和推荐任务。这个版本同时提供了新的一些pipeline用于直接获取图表示和搭建推荐应用。欢迎大家参加我们在KDD 2021上的tutorial，时间是8月14号上午10:30 - 12:00（北京时间）。 更多的内容可以查看 https://kdd2021graph.github.io/. 🎉
-
 <details>
 <summary>
 历史
 </summary>
 <br/>
 
+- 最新的 **v0.4.1 release** 增加了深层GNN的实现和推荐任务。这个版本同时提供了新的一些pipeline用于直接获取图表示和搭建推荐应用。欢迎大家参加我们在KDD 2021上的tutorial，时间是8月14号上午10:30 - 12:00（北京时间）。 更多的内容可以查看 https://kdd2021graph.github.io/. 🎉
+
 - 最新的 **v0.4.0版本** 重构了底层的数据存储（从`Data`类变为`Graph`类），并且提供了更多快速的算子来加速图神经网络的训练。这个版本还包含了很多图自监督学习的算法。同时，我们很高兴地宣布我们将在8月份的KDD 2021会议上给一个CogDL相关的tutorial。具体信息请参见[这个链接](https://kdd2021graph.github.io/). 🎉
 
 - CogDL支持图神经网络模型使用混合专家模块（Mixture of Experts, MoE）。 你可以安装[FastMoE](https://github.com/laekov/fastmoe)然后在CogDL中尝试 **[MoE GCN](./cogdl/models/nn/moe_gcn.py)** 模型!

cogdl/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.5.2"
+__version__ = "0.5.3"
 
 from .experiments import experiment
 from .pipelines import pipeline

docs/source/index.rst

@@ -16,6 +16,7 @@ We summarize the contributions of CogDL as follows:
 ❗ News
 ------------
 
+- The new **v0.5.3 release** supports mixed-precision training by setting \textit{fp16=True} and provides a basic [example](https://github.com/THUDM/cogdl/blob/master/examples/jittor/gcn.py) written by [Jittor](https://github.com/Jittor/jittor). It also updates the tutorial in the document, fixes downloading links of some datasets, and fixes potential bugs of operators. 
 - The new **v0.5.2 release** adds a GNN example for ogbn-products and updates geom datasets. It also fixes some potential bugs including setting devices, using cpu for inference, etc.
 - The new **v0.5.1 release** adds fast operators including SpMM (cpu version) and scatter_max (cuda version). It also adds lots of datasets for node classification. 🎉
 - The new **v0.5.0 release** designs and implements a unified training loop for GNN. It introduces `DataWrapper` to help prepare the training/validation/test data and `ModelWrapper` to define the training/validation/test steps.